Education

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

S. B. Dias, S. J. Hadjileontiadou, et al.

Discover DeepLMS, a groundbreaking deep learning model developed by Sofia B. Dias, Sofia J. Hadjileontiadou, José Diniz, and Leontios J. Hadjileontiadis that accurately predicts the quality of interaction with Learning Management Systems. With its impressive performance, including an average testing RMSE of less than 0.009, this model not only enhances learner experience but also equips educators with powerful evaluation tools.

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Abstract

This paper introduces DeepLMS, a novel deep learning model that predicts the quality of interaction (QoI) with Learning Management Systems (LMS). Using Long Short-Term Memory (LSTM) networks, DeepLMS achieves high accuracy in QoI prediction, showing average testing Root Mean Square Error (RMSE) < 0.009 and a correlation coefficient r≥ 0.97 (p<0.05) across three datasets collected before and during the COVID-19 pandemic. DeepLMS provides personalized feedback to learners and offers educators an additional evaluation tool beyond content-related assessments.

Publisher

Scientific Reports

Published On

Nov 16, 2020

Authors

Sofia B. Dias, Sofia J. Hadjileontiadou, José Diniz, Leontios J. Hadjileontiadis

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